Methods of identifying and/or assessing cardiac arrhythmias using an implantable medical device

ABSTRACT

Apparatus using one or more modes of statistical analysis with one or more monitored parameters of a patient&#39;s heart to identify and/or assess arrhythmias. Through use of the one or more modes of statistical analysis, a medical professional can be aided during evaluation of patient data for diagnosis of the patient. At least one of the monitored parameters may include one or more values used representatively for storage intervals of a selected length. As such, for each storage interval, a value may be determined for the one monitored parameter occurring at an upper percentile and a lower percentile. In addition, a median value may be determined for the one monitored parameter for each storage interval. Over a plurality of the storage intervals, these determined values can be used in one or more modes of statistical analysis to better identify and assess the arrhythmias.

RELATED APPLICATION

This application is a divisional of U.S. patent application Ser. No.11/342,397, filed Jan. 30, 2006 entitled “METHODS OF IDENTIFYING AND/ORASSESSING CARDIAC ARRHYTHMIAS USING AN IMPLANTABLE MEDICAL DEVICE”,herein incorporated by reference in its entirety.

BACKGROUND

The present invention relates to medical devices, and, moreparticularly, to implantable medical devices.

Typically, patients with heart failure have a reduced capacity formyocardial function. The heart is unable to adequately meet themetabolic demands of the body by providing the appropriate blood flow.This may result in increased blood pressure (afterload), and increasedvolume retention (preload). Thus, common symptoms of heart failure orventricular dysfunction include fatigue, which is caused by the lowcardiac output, and edema and swelling, which is caused by fluidoverload.

In patients with heart failure, on-going ambulatory monitoring of theheart conditions can be an important factor in successful cardiacdisease management. Such ambulatory monitoring of the patient can ofteninvolve the collection of parameters such as heart rate, pressure,temperature, etc. As is known, the monitoring of one or more of theseparameters can be achieved through the use of one or more implantablemedical devices (IMDs), such as pacemakers, defibrillators, monitors,etc. Following transfer of these parameters from the IMD(s) to aclinical center, a medical professional, after subsequent analysis, cangenerally provide final diagnoses concerning the patient.

However, quite often, the collection of these parameters can be limitedby the location of the IMD electrode and/or the fixed storage memorysize of the IMD. In turn, it can be difficult for the medicalprofessional to identify arrhythmias based on these parameters becauseof the above limiting factors or other factors. As such, it can also bedifficult for the medical professional to routinely provide accuratediagnoses for the patient because of uncertainty regarding whether theparameters are representative of a worsening state of the patient'sheart, or conversely, due to an arrhythmic event.

Certain embodiments of the invention are directed to overcoming, or atleast reducing the effects of, one or more of the limitations set forthabove.

SUMMARY OF THE INVENTION

Embodiments of the invention involve using one or more monitoredparameters of a patient's heart to identify and/or assess arrhythmias.As a result, a medical professional can be aided during evaluation ofpatient data for diagnosis of the patient. In some of the embodiments,at least one of the monitored parameters includes one or more valuesused representatively for storage intervals of a selected length. Insuch embodiments, for each storage interval, a value can be determinedfor the one monitored parameter occurring at an upper percentile and alower percentile. In addition, a median value can be determined for theone monitored parameter for each storage interval. Over a plurality ofthe storage intervals, these determined values can be used in one ormore modes of statistical analysis to better identify and assess thearrhythmias.

In some embodiments, the at least one monitored parameter includes heartrate. In such embodiments, a “criteria mode” of statistical analysis canbe used to determine when one of the determined values of the at leastone monitored parameter exceeds a designated setting. If, previous tothis one determined value, the one monitored parameter is also found tohave increased by a certain set amount, the “criteria mode” isconfigured to indicate the presence of an arrhythmic event. In furtherembodiments, a “ratio mode” of statistical analysis can be used todetermine the distances between one or more of the determined values ofthe at least one monitored parameter. By calculating a ratio of thesedistances, the “ratio mode” is configured to indicate the presence of anarrhythmic event and/or a duration of the arrhythmic event.

In further embodiments, one of the monitored parameters includes cardiacpressure data. In such embodiments, a “variability mode” of statisticalanalysis can be used with respect to the pressure data with one or moreother monitored parameters to further confirm a perceived arrhythmicevent.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a system including an implantable medicaldevice (IMD) in accordance with certain embodiments of the invention.

FIG. 2 is an exemplary plot of a trend line for upper percentile heartrate values over a time period in accordance with certain embodiments ofthe invention.

FIG. 3 is a flow chart illustrating a method of identifying and/orassessing arrhythmic events from data as exemplified in FIG. 2.

FIG. 4 is an exemplary plot of trend lines for upper percentile, median,and lower percentile heart rate values over a time period in accordancewith certain embodiments of the invention.

FIG. 5 is a series of plots including the plot of FIG. 4 and anexemplary plot of high rate burden over the time period of the plot ofFIG. 4 in accordance with certain embodiments of the invention.

FIG. 6 is a flow chart illustrating a method of identifying and/orassessing arrhythmic events from data as exemplified in FIG. 4.

FIG. 7 is a flow chart illustrating a method of identifying and/orassessing arrhythmic events from data as exemplified in FIG. 5.

FIG. 8 is a exemplary series of plots of heart rate and pressureparameters over a time period in accordance with certain embodiments ofthe invention.

FIG. 9 is a flow chart illustrating a method of providing an indicatorto identifying and/or assessing arrhythmic events from data asexemplified in FIG. 8.

DETAILED DESCRIPTION

The following discussion is presented to enable a person skilled in theart to make and use the present teachings. Various modifications to theillustrated embodiments will be readily apparent to those skilled in theart, and the generic principles herein may be applied to otherembodiments and applications without departing from the presentteachings. Thus, the present teachings are not intended to be limited toembodiments shown, but are to be accorded the widest scope consistentwith the principles and features disclosed herein. The followingdetailed description is to be read with reference to the figures, inwhich like elements in different figures have like reference numerals.The figures, which are not necessarily to scale, depict selectedembodiments and are not intended to limit the scope of the presentteachings. Skilled artisans will recognize the examples provided hereinhave many useful alternatives and fall within the scope of the presentteachings.

FIG. 1 is a simplified schematic diagram representation of a system inaccordance with certain embodiments of the invention. As shown, an IMD10 is located in a patient 12. The IMD 10 includes one or more leadsextending between the IMD 10 and the heart 16 of the patient 12. Forexample, as shown, one lead 14 can extend to one of the ventricles,e.g., the right ventricle 18. As such, the IMD 10 can be a singlechamber device; however, the invention should not be limited as such.Instead, as mentioned above, it is to be appreciated that the IMD 10 canalternatively, or in combination, include other leads (e.g., leads 14′and/or 14″) extending to other distinct areas of the heart (e.g.,respectively, to one or more of the atria 20, 20′ and/or the leftventricle 18′ of the heart 16). As such, the IMD 10 can include anymedical device having single or multi-chamber functionality.Alternatively, other devices such as implantable drug delivery devicescan also be adapted for use with the current invention.

In certain embodiments, the IMD 10 is an implantable hemodynamic monitor(IHM). However, it is to be appreciated that the invention should not belimited to such a device. Generally, any form of implantable medicaldevice suitable for storing telemetered signals or physiological datacould be used, as known in the art. In certain embodiments in which anIHM is utilized, the monitor can include circuitry for data storage andfor recovering and processing of cardiac parameters, such as pressure,electrogram, heart rate, core temperature, and activity data. Such anIHM can generally be used in patients with Chronic Heart Failure (CHF),undergoing serial clinical management, and is typically used tocomplement existing CHF therapies and disease management regimens inorder to provide precise therapy management and early intervention byremote monitoring of impending decompensation, so as to improve qualityof life. In addition, such an IHM generally contains an operating systemthat may employ a microcomputer or a digital state machine for timing,sensing, data storage, recovery and processing of pressure, electrogram,heart rate and other related data, to thereby monitor the hemodynamicenvironment.

An exemplary IHM and its associated leads and circuitry are described incommonly-assigned U.S. Pat. Nos. 5,535,752; 5,564,434; 6,024,704; and6,152,885, which are incorporated herein by reference in relevant parts.Other pacing systems known in the art may be adapted for use in thealternative. The IMD 10 can additionally, or in the alternative, includecardioversion/defibrillation circuitry as described in commonly-assignedU.S. Pat. Nos. 5,193,535, and 5,314,430, which are incorporated hereinby reference in relevant parts. The leads and circuitry disclosed in theabove-incorporated, commonly assigned, '752 and '434 patents can beemployed to record EGM and absolute blood pressure values over certaintime intervals. In certain embodiments, the recorded data can beperiodically telemetered from the IMD 10 (e.g., to a programmer) so asto be later evaluated by a physician or other medical professional. Incertain embodiments, the data is transmitted from the IMD 10 during atelemetry session. It should be appreciated that such sessions caninvolve communications with programmers and/or networks (e.g., via weblinks) so as to enable the provision of the data from the IMD 10 to thephysician or medical professional.

With continued reference to FIG. 1, the IMD 10 can be implantedsubcutaneously, between the skin and the ribs of the patient 12. Otherimplantation sites may be used if appropriate. As described above, incertain embodiments, the lead 14 is passed through a vein into the rightventricle 18 of the heart 16. The distal end of the lead 14 or cathetermay have a tip electrode 22 contacting the interior of the heart 16. Ina multipolar configuration, a second ring electrode 24 may be spacedfrom the tip electrode 22. Each of these electrodes is connected to thecircuitry contained in the IMD 10. Alternatively, a unipolar mode may beused wherein a portion of the metallic enclosure or “can” of the IMD 10can form an electrode surface 26. As such, the EGM signal is measuredbetween this surface 26 and an implanted electrode, such as the tipelectrode 22. In yet another embodiment, a Subcutaneous Electrode Array(SEA) such as electrodes 28 and 30 can be located on, but electricallyisolated from, the housing of the IMD 10, such as disclosed in U.S. Pat.No. 5,331,966, incorporated herein by reference in relevant part.

The lead 14 is shown to further include a pressure sensor 32. Ifdesired, an additional lead (not shown) coupled to IMD 10 may beprovided to carry the pressure sensor. In certain embodiments, thepressure sensor 32 is located within the right ventricle 18, although itmay instead be located within the left ventricle 18′. Pressure sensorsand accompanying circuitry that may be adapted for use with embodimentsof the invention are described in commonly-assigned U.S. Pat. Nos.5,353,752, 5,353,800, 5,564,434, 5,330,505, and 5,368,040 which areincorporated herein by reference in their relevant parts.

Generally, an IHM can be used for recording a variety of hemodynamicparameters in a patient with heart failure, for example, including rightventricular (RV) systolic and diastolic pressures (RVSP and RVDP),estimated pulmonary artery diastolic pressure (ePAD), pressure changeswith respect to time (dP/dt), heart rate, activity, and temperature.Some parameters can be derived from others, rather than being directlymeasured. For example, the ePAD parameter can be derived from RVpressures at the moment of pulmonary valve opening, and heart rate canbe derived from information in an intracardiac electrogram (EGM)recording. Hemodynamic pressure parameters can be obtained by using apressure sensor mounted on a lead to measure intracardiac bloodpressures, including absolute and/or relative pressures. U.S. Pat. No.6,865,419 to Mulligan et al., incorporated herein by reference in itsrelevant part, discloses a method of deriving mean pulmonary arterialpressure (MPAP) from an IHM.

Information collected by the IMD 10 can be retrieved and transmitted toan external device, or to a patient management network, or to adatabase, using various transmission methods including the Internet. Forexample, a patient can activate the device to retrieve and transmit thedata stored in the IMD 10 to a remote system, where additionalprocessing may be performed on the data. This retrieval and transmissionof stored data can be done on a periodic basis, e.g., once per week, toprovide a convenient method of “continuous” monitoring of a patient.Stored data retrieved from the IMD 10 and transmitted to a remote systemcan be available for transfer to a clinical center for review by amedical professional. In certain embodiments, data is transferable to aninternet-compatible central patient management network for remotemonitoring. A bi-directional communication system that is network,Internet, intranet and worldwide web compatible to enable chronicmonitoring based on data obtained from implantable monitors is generallydisclosed in International Publication No. WO 01/70103 A2, to Webb etal, incorporated herein by reference in its relevant part.

As described above, the data stored by the IMD 10 can include variousparameters that are continuously monitored, for example, the IMD 10 mayrecord intracardiac EGM data at sampling rates as fast as 256 Hz orfaster. In certain embodiments, the IMD 10 can alternately store summaryforms of data that allows for storage of data representing longerperiods of time. For example, in certain embodiments, if the IMD 10 isan IHM, hemodynamic pressure parameters can be summarized by storing anumber of representative values that describe the hemodynamic parameterover a given storage interval. The mean, median, an upper percentile,and a lower percentile are examples of representative values that can bestored by the IMD 10 to summarize data over an interval of time (e.g.,the storage interval). In this manner, the memory of the IMD 10 can beused to provide hourly, weekly or monthly (or longer) views of the datastored depending on the length of the storage interval selected. Thedata buffer of the IMD 10, for example, may acquire data sampled at a256 Hz sampling rate over the certain storage intervals, and the databuffer can be cleared out after the selected representative valuesduring that corresponding storage interval are stored. It should benoted that other parameters measured by the IMD 10 can also besummarized using the above techniques, for example, for parameters suchas heart rate, activity level, and temperature.

As described above, in certain embodiments, the IMD 10 can providesummary forms of data for one or more of the parameters monitored by theIMD 10. In certain embodiments, one of these monitored parametersincludes heart rate. As such, as described above, one or more heart ratevalues can be selected to be representative for storage intervals of acertain designated length. In certain embodiments, the designated lengthof the storage intervals is five minutes; however, the invention shouldnot be limited to such. As should be appreciated, the storage intervallength is generally variable; however, certain trade-offs need to betaken into account prior to its designation. For example, if the storageinterval length is set to be short in duration, the resolution of thedata is increased; however, the storage rate of the data is alsoincreased. As such, a better representation of the data as a whole canbe provided, but the rate at which the memory of the IMD 10 fills up isincreased, requiring transfer of the data from the IMD 10 to occur morefrequently. Conversely, if the storage interval length is set to be longin duration, the resolution of the data is decreased; however, thestorage rate of the data is decreased. As such, the rate at which thememory of the IMD 10 fills up is decreased, prompting downloads of thedata from the IMD 10 to occur less frequently, but the representation ofthe data as a whole is generally compromised. In turn, the datarepresentation may lead to misidentification of arrhythmic events. Inturn, as should be appreciated, the setting of the storage intervallength requires careful consideration.

In certain embodiments, the one or more heart rate values selected forstorage by the IMD 10 includes at least an upper percentile value (e.g.,a 94^(th) percentile value). An exemplary plot of a trend line for theupper percentile values for a patient (each corresponding to a storageinterval length) over a certain period of time is illustrated in FIG. 2.As shown, the trend line of the upper percentile values includes heartrates ranging from about eighty beats per minute to about one-hundredseventy-five beats per minute. As also exemplified, the trend line forthe upper percentile values is shown over a thirteen-day duration (e.g.,as shown, beginning on 7/27 and ending on 8/08). In addition, as shown,there are approximately twenty-four values recorded for every two daysof operation of the IMD. As such, for this example, the storage intervallength set for the IMD is generally about two hours. However, as shouldbe appreciated from the above discussion, this storage interval lengthmay be considered too long, resulting in too much of a decreasedresolution of the data for adequate analysis purposes. As such, itshould be appreciated that the plot of FIG. 2 is simply provided forillustrative purposes, and the invention should not be limited to such.

As described herein, embodiments of the invention involve using modes ofstatistical analysis to identify and/or assess arrhythmic events (e.g.,atrial fibrillation). For example, with respect to single chamber IMDs,a lead of the IMD can be typically inserted in one of the heart chambers(e.g., the right ventricle). As such, the modes of analysis can be usedto identify and/or assess arrhythmias occurring in the ventricles and/orthe atria of the heart via data collected from such single lead. Withrespect to multi-chamber IMDs, multiple leads of the IMDs are typicallyinserted respectively in one of the ventricles and the atria. While themultiple leads may generally provide enough data to identify and/orassess arrhythmias occurring in the ventricles and/or atria of theheart, the modes of analysis can be used as a confirmatory mechanism. Inturn, such modes of statistical analysis can aid a medical professionalin evaluating patient data to diagnose the patient. In certainembodiments, the modes of statistical analysis can be provided outsidethe IMD 10, e.g., by a programmer, monitor, or other processing devicelocated remote from the IMD 10. As such, the modes of statisticalanalysis can be performed without involving further processing and/oradditional circuitry for the IMD 10. However, the invention should notbe limited to such. It should be appreciated that such modes ofstatistical analysis can just as well be performed by the IMD 10 andstill be within the spirit of the invention.

In certain embodiments, one such mode of statistical analysis isreferenced herein as the “criteria mode”. The “criteria mode” involvesdetermining whether the data meets certain criteria so as to signal thepresence of an arrhythmic event. In certain embodiments, a firstcriterion involves determining whether a value exceeds a certain setheart rate threshold. As should be appreciated, during an arrhythmicevent, the heart rate is generally well above the normal heart rate of apatient. Since patients have differing normal heart rates, the thresholdis carefully selected so as to correspond to the patient's normal heartrate, and can be altered from time to time by the medical professionalover the patient's life. For example, one patient's normal heart ratemay be seventy beats per minute. As such, the heart rate thresholdselected for such patient may be two times their normal heart rate, orone-hundred forty beats per minute. In turn, when using the “criteriamode”, a determination is made with respect to the upper percentilevalue determined for each storage interval as to whether thecorresponding value exceeds this heart rate threshold. With reference toFIG. 2, going from left to right on the x-axis with respect to the upperpercentile values plotted, it is illustrated that the first value thatexceeds this selected threshold occurs roughly on 7/29. As such, thisvalue, referenced as 34, satisfies the first criteria.

In turn, the “criteria mode” determines whether a second criterion ismet so as to indicate an arrhythmic event. As should be appreciated, theonset of an arrhythmic event is often represented by a dramatic increaseof the patient's heart rate from normal. As such, in certainembodiments, the “critical mode” determines whether the heart ratedifference between value 34 and values occurring prior to value 34exceed a certain designated amount. Again, as described above, eachpatient is different with respect to his or her cardiac condition. Assuch, the designated difference amount may be selected higher for somepatients than for others. Again, it should be appreciated that suchdifference amount needs to be carefully selected based on the patient'scondition, and can be altered from time to time by the medicalprofessional over the patient's life. In one example, the differenceamount may be selected by the medical professional to be forty beats perminute.

As such, in certain embodiments, the “criteria mode” examines one ormore values prior to the value 34 to determine whether the difference inheart rate between the value 34 and any one of the prior values exceedsthe set difference amount. As can be appreciated, one would have to goback five values prior to value 34 to show a value 36 that meets thissecond criteria. However, as described above, since the storageintervals shown by the plot of FIG. 2 generally represent two hourdurations, it is safe to say that the ten hour duration between values36 and 34 signals are probably not related by an arrhythmic event. Assuch, one must also carefully select the quantity of values thestatistical analysis is to consider prior to a value meeting the firstcriteria in order to adequately determine whether an arrhythmic eventhas occurred. In the case of the plot of FIG. 2, one is likely to onlygo back one value and see if the difference between a first valuemeeting the first criteria and the difference between the first valueand the value occurring just prior to the first value meets the secondcriteria. Such a case is shown by value 38 occurring roughly on 7/31. Asshown, the value 38 is above the set heart rate threshold of one-hundredforty beats per minute (meeting the first criteria) and the differencebetween the value 38 and a value 40 occurring just prior to the value 38exceeds the forty beats per minute difference amount (meeting the secondcriteria). As such, by the “criteria mode” of statistical analysis, thevalue 38 would indicate conditions for an arrhythmic event.

As shown by the above-described statistical analysis, the trade-offconcerning storage interval length has an obvious effect on thedetermination of whether the second criteria is met. In turn, the numberof values the “criteria mode” checks in determining whether the secondcriteria is met should be decided with consideration being made to thelength of the storage interval length. The above example indicates ascenario in which the number of values needs to be limited due to theoccurrence of potential false positives for the statistical analysis.However, in certain embodiments, when the storage interval length isselected to be five minutes (as described above), the number of valuesthat can then be considered in the second criteria analysis can beexpanded from just the prior value. In such certain embodiments, thenumber of prior values considered can be three prior values; however, asshould be appreciated, the invention should not be limited to such. Inaddition, in order to filter out other false positives for thestatistical analysis, an activity sensor would generally be used tofilter out data that is brought on by physical activity by the patient12. As should be appreciated, such activity sensors can be used tocharacterize and mark and/or filter out data that is the result of suchphysical activity by the patient 12. As such, the marked or filtereddata is eliminated prior to or alternatively during the statisticalanalysis of the data.

FIG. 3 shows a flowchart for the above “criteria mode” of statisticalanalysis. As shown, a first step 42 involves a manipulation of one ormore cardiac parameters (by the summarizing function of the IMD 10described above) and storage of the values within the IMD 10. A nextstep 44 involves a gathering of certain data “Xu” of the one or morecardiac parameters stored within the IMD 10. As mentioned above, thisgathering can be represented by the IMD 10 gathering such data “Xu” fromthe memory of the IMD 10 for analysis by the IMD 10, or conversely, bythe IMD 10 transferring such data “Xu” from the memory of the IMD 10 toa programmer, monitor, or other processing device for subsequentanalysis. As illustrated above in FIG. 2, in certain embodiments, thedata “Xu” gathered can involve upper percentile heart rate values (e.g.,94^(th) percentile values) for a specific time period (involving aplurality of consecutive storage intervals, wherein each storageinterval is denoted an increment of “i”). However, the invention shouldnot be limited to such. For example, in other embodiments, the data “Xu”can involve averaged heart rate values (e.g., upper percentile values),where each averaged heart rate value is derived from heart rate measuresfrom a plurality of consecutive storage intervals over the specificperiod of time (with each plurality of consecutive storage intervalsbeing denoted an increment of “i”). A further step 46 involves goingthrough the data “Xu” sequentially (e.g., over the “i” storageintervals) to determine if and when the first criteria of the abovestatistical analysis is met (with “i” being set to one). As describedabove, the first criteria involves determining if the corresponding data“Xu” exceeds a set heart rate threshold “X”. As exemplified with respectto FIG. 2, in certain embodiments, such heart rate threshold “X” may beset as one-hundred forty beats per minute.

If a value “Xu(i)” within the data “Xu” exceeds a set heart ratethreshold “X”, thereby meeting the first criteria, a next step 48involves determining whether the difference between the value “Xu(i)”and any of “Xu(i−Z)” values of the data “Xu” occurring prior to thevalue “Xu(i)” exceed a set threshold amount “Y”. As exemplified withrespect to FIG. 2, “Z”, which corresponds to the quantity of prior datavalues checked, may be set to one, and “Y” may be set to forty beats perminute; however, as described above, the invention should not be limitedto such. If any of the differences between the value “Xu(i)” and the“Xu(i−Z)” values of the data “Xu” exceed the set threshold amount “Y”, anext step 50 involves the “criteria mode” determining that conditionshave been met for an arrhythmic event. As such, during step 50, acounter is triggered, so as to keep track of the number of such eventsoccurring during analysis of the data “Xu”. In addition, a furthercounter is triggered to increment “i” by one for subsequent evaluationof the next value “Xu(i+1)” of the data “Xu”. Subsequently, the analysisis looped back between steps 44 and 46 for analysis involving the nextdata value “Xu(i+1). As should be appreciated, if the criteria is notmet in either of the steps 46 or 48 (respectively corresponding to thesteps involving the first and second criteria), the further counter istriggered to increment “i” by one in step 51 for subsequent evaluationof the next value “Xu(i+1)” of the data “Xu”. Subsequently, the analysisis looped back between steps 44 and 46 for analysis involving the nextdata value “Xu(i+1)”.

Using the same summarizing principles as described above for one or moreof the cardiac parameters stored by the IMD 10, further modes ofstatistical analysis can be used to identify and/or assess arrhythmicevents (e.g., atrial fibrillation). One further mode of statisticalanalysis is the “ratio mode”. In certain embodiments, for selectedstorage interval lengths, an upper percentile value (e.g., a 94^(th)percentile value), a median value, and a lower percentile value (e.g., a6^(th) percentile value) are determined for the one or more cardiacparameters and stored within the IMD 10 for each storage interval. Incertain embodiments, one cardiac parameter involves heart rate. Asgraphically illustrated in FIG. 4, the heart rate values can be plottedto show trend lines for each value over a designated period of time. Asexemplified in FIG. 4, the trend lines for each value are shown over aseven-day duration (e.g., as shown, beginning on 6/26 and ending on7/02). As should be expected, the trend line 52 for the upper percentilevalues is the highest curve on the plot, the trend line 54 for themedian values is the next highest curve on the plot, and the trend line56 for the lower percentile values is the lowest curve on the plot.

As shown, the three trend lines 52, 54, and 56 are continuously in fluxas to their relative distances with each other. In turn, thedistribution of the corresponding heart rate values collected during thedesignated period of time of the plot is illustrated. For example, asthe median trend line 54 approaches the upper percentile trend line 52,the majority of values are located closer to the upper percentile. Incertain embodiments, the “ratio mode” involves a determination of thedistribution skewness of the heart rate values. In turn, for eachstorage interval, the “ratio mode” involves determining a distancebetween the upper percentile value and the median value and determininga distance between the median value trend line and the lower percentilevalue. By subsequently computing a ratio of these distances for thecorresponding storage interval, the “ratio mode” can be used to identifythe presence of an arrhythmic event. By further tracking the ratios fora number of consecutive storage intervals, the “ratio mode” can be usedto provide an estimation as to the duration of the arrhythmic event.

As should be appreciated, a plurality of different ratios can bedetermined by making comparisons of different results. For example, incertain embodiments, for each storage interval, the difference betweenthe upper percentile value and the median value can be compared to thedifference between the median value and the lower percentile value;however, the invention should not be limited as such. During anarrhythmia, it should be appreciated that the median trend line 54generally rises closer to the upper percentile trend line 52 and movesaway from the lower percentile trend line 56. This is because the heartrate of a patient is generally elevated fairly consistently over such anevent. As such, in calculating the ratio using the above-describedexemplary calculation (when dividing the distance between the upperpercentile value and the median value by the distance between the medianvalue and the lower percentile value), the result tends to be a valueless than unity.

For example, with reference to FIG. 4, for the storage intervalcorresponding to the first spike in the plateau of the upper percentile(referenced by line 58), the upper percentile value is about one-hundredsixty-three beats per minute, the median value is about one-hundredforty-two beats per minute, and the lower percentile value is aboutninety-two beats per minute. As such, in using the difference and ratiocalculation exemplified above, a ratio of 0.42 is calculated. Incomparison, with further reference to FIG. 4, for the storage intervallength corresponding to the previous spike of the upper percentile trendline (referenced by line 60), the upper percentile value is abouteighty-four beats per minute, the median value is about seventy-twobeats per minute, and the lower percentile value is about sixty-fourbeats per minute. In turn, the same comparison of these differencesresults in a ratio of 1.50. In certain embodiments, for many patients,it has been found that the ratio calculated in normal conditions for theheart tends to be about 1.00. As such, the statistical analysis can beused to indicate conditions representing an arrhythmia (e.g., when theratio starts to approach zero) and/or the general duration of thearrhythmia. For instance, a low ratio indicates that the arrhythmialasted a higher percentage of the storage interval, indicating a higherburden. In addition, a low ratio over multiple consecutive intervalsindicates a long-lasting arrhythmia. As discussed herein, informationfrom an activity sensor or other type of sensor may be used to helpconfirm that the low ratio represents an arrhythmia and not just a highrate due to physical exertion.

It should be appreciated that over time, certain ratios or ranges ofratios may be found to be linked to specific arrhythmias. As such, itshould be appreciated that the above statistical analysis can be usednot only to identify conditions representing arrhythmias and/or thegeneral duration of arrhythmias, but also in identifying the specificarrhythmia that affected the patient based on its ratio or range ofratios during such event. In addition, while the above embodimentdescribes one calculation for determining the ratio based on a certaincomparison of the distances, it should be appreciated that othercalculations can be used alternatively. It should be appreciated thatwhile using different calculations may result in different ratios, suchwould still be within the spirit of the invention when used to offerinformation related to the detection and/or assessment of arrhythmicevents.

With further reference to FIG. 4, the “ratio mode” of statisticalanalysis can be enhanced to further aid medical professionals inproviding diagnoses for the patient. In certain embodiments, the “ratiomode” can be used to calculate a distance between values on the upperand lower percentile trend lines 52, 56 for one or more storage intervallengths. As should be appreciated, such a distance (either alone or whencompared with the distances between the upper percentile value andmedian value and/or the distance between the median value and the lowerpercentile value), when taken over the length of the time period of thetrend lines 52, 56, would enable the “ratio mode” to provide informationas to the variability of the patient's heart rate. This variabilityinformation, when combined with the calculated ratio information, canfurther provide a medical professional insight as to the condition ofthe patient's heart.

In certain embodiments, the “ratio mode” can also be further enhanced bycontinuously averaging the deviation of the calculated ratios from afixed or dynamic base line ratio (e.g., a long-term average or a fixedthreshold) to detect sudden shifts in the relationship between the trendlines 52, 54, and 56. In certain embodiments, this continuous averagingcan be provided via a cumulative sum. In turn, any deviation betweenthese two ratios is averaged over a certain historical period of timeand such averaged deviation is cumulatively added to deviationssimilarly calculated for prior storage intervals. As such, when thecalculated ratio deviates from the base line ratio for any extendedperiod of time (e.g., exhibiting high rate burden), the cumulative sumis found to show a significant output. The output of the cumulative sumis generally found to be proportional to the magnitude of high rateburden, or length of the event. As such, using the output of thecumulative sum as a basis, the high rate burden can, in turn, beplotted. Such a graphical representation is shown in FIG. 5, with thehigh rate burden being plotted across the same time period that is usedfor the trend lines 52, 54, and 56 of FIG. 4.

As illustrated, the upper plot of FIG. 5 is the same as shown in FIG. 4;however, the lower plot represents the high rate burden exhibited forthe trend lines 52, 54, and 56 of the upper plot based on the output ofthe cumulative sum. As illustrated, the high rate burden is detected inthe regions of FIG. 4 in which the median trend line 54 is closest tothe upper percentile trend line 52 and furthest from the lowerpercentile trend line 56. As described with respect to the “ratio mode”,based solely on the ratio calculations, such regions are generallyindicative of conditions representing an arrhythmia because thecalculated ratio for such storage intervals tend to approach zero (e.g.,below 0.5). However, using the lower plot of FIG. 5, one can see highrate burden is similarly detected during such regions via the output ofthe cumulative sum. In turn, the lower plot provides further evidencefor the “ratio mode” of conditions representing an arrhythmia. Inaddition, by the magnitude of the high rate burden illustrated in thelower plot, the enhanced “ratio mode” can be used to further indicatethe length in time of the arrhythmic event. For example, for high rateburden values that are found to exceed a selected threshold, the “ratiomode” would indicate the presence of an arrhythmia.

FIG. 6 shows a flowchart for one embodiment of the above “ratio mode” ofstatistical analysis. As shown, a first step 62 involves a manipulationof one or more cardiac parameters (by the summarizing function of theIMD 10 described herein) and storage of the values within the IMD 10. Anext step 64 involves a gathering of certain data (e.g., “Xu”, “Xm”, and“Xl”) of the one or more cardiac parameters stored within the IMD 10. Asmentioned above, this gathering can be represented by the IMD 10gathering such data from the memory of the IMD 10 for analysis by theIMD 10, or conversely, by the IMD 10 transferring such data from thememory of the IMD 10 to a programmer, monitor, or other processingdevice for subsequent analysis. As illustrated above in FIG. 4, incertain embodiments, the data “Xu”, “Xm”, and “Xl” gathered involvesupper percentile heart rate values (e.g., 94^(th) percentile values),median heart rate values, and lower percentile values (e.g., 6^(th)percentile values) for a specific time period (involving a plurality,denoted by “i”, of consecutive storage interval lengths).

A further step 66 involves going through the data “Xu”, “Xm”, and “Xl”sequentially (over the “i” storage intervals) to determine differencesof the values. In turn, a ratio is determined for the correspondingstorage interval. As described above, one ratio calculation that can beused is based on a comparison of the difference between the upperpercentile value and the median value and the difference between themedian value and the lower percentile value. Based on the value of theratio, a next step 68 involves the “ratio mode” determining whether ornot the calculated ratio is below Ar (an arrhythmic event indicatorratio threshold). In certain embodiments, such ratio can be generallyabout 0.5; however the invention should not be limited to such. Asdescribed above, generally when the ratio is found to approach zero (soas to be below Ar), such ratio can be indicative of an arrhythmic event.As such, during step 70, a counter is triggered, so as to keep track ofthe duration of such arrhythmic event occurring during analysis of thedata “Xu”, “Xm”, and “Xl”. In addition, a further counter is triggeredto increment “i” by one for subsequent evaluation of the next value“Xu(i+1)” of the data “Xu”, “Xm(i+1)” of the data “Xm”, and “Xl(i+1)” ofthe data “Xl”. Subsequently, the analysis is looped from step 70 backbetween steps 64 and 66 for analysis involving the next data values“Xu(i+1)”, “Xm(i+1)”, and “Xl(i+1)”. As should be appreciated, if thecalculated ratio is determined as not being below AR in the step 68, theduration counter is reset and the further counter is triggered toincrement “i” by one in step 72 for subsequent evaluation of the nextvalues “Xu(i+1)”, “Xm(i+1)”, and “Xl(i+1)” of the data. Subsequently,the analysis is looped from step 68 to step 72 and subsequently backbetween steps 64 and 66 for analysis involving the next data values.

In certain embodiments, as shown in step 66, the distance between theupper percentile value “Xu(i)” and the lower percentile value “Xl(i)”can also be calculated in combination with the other differencesmentioned above. As described above, such a distance value (either aloneor when compared with the distances between the upper percentile valueand median value and/or the distance between the median value and thelower percentile value) would enable the statistical analysis to provideinformation as to the variability of the patient's heart rate.

FIG. 7 shows a flowchart similar to the flowchart of FIG. 6 with steps62-66, 70, and 72 being the same. Step 67 involves comparing the ratiocalculated in step 66 with a dynamic base line ratio (e.g., a long-termaverage or a fixed threshold) for the patient. The deviation betweenthese ratios is subsequently averaged over a certain historical periodof time and such averaged deviation is cumulatively added to deviationssimilarly calculated for previous storage intervals. As described above,in certain embodiments, this aggregate functionality is enabled via theuse of a cumulative sum. As further described above, when the calculatedratio deviates from the base line ratio for any extended period of time(e.g., exhibiting high rate burden), it is found that the output of thecumulative sum begins to show a significant output. For example, theoutput of the cumulative sum “S(i)” can be computed with the followingequation:“S(i)”=“S(i−1)”+y(i)−k,where k=a limiting constant, andy(i)=C[x(i)−μ(i)],where C=an averaging or normalization constant,x(i)=the calculated ratio, andμ(i)=the baseline ratio.

The output of such cumulative sum is found to be proportional to amagnitude of the high rate burden. As such, in step 69, the output ofthe cumulative sum “S(i)” is compared to a high rate burden threshold,HRBt. If the output exceeds the threshold, the “ratio mode” can furtherindicate the presence of an arrhythmia. As such, during step 70, thesame counters described above with respect to FIG. 6 are triggered.Subsequently, the analysis is looped from step 70 back between steps 64and 66 for further analysis. As should be appreciated, if the criteriais not met in the step 69, the duration counter is reset and the furthercounter is triggered to increment “i” by one in step 72 for subsequentevaluation of the next values “Xu(i+1)”, “Xm(i+1)”, and “Xl(i+1)” of thedata. Subsequently, the analysis is looped back step 69 through step 72and subsequently between steps 64 and 66 for analysis involving the nextdata values.

As described above, further modes of statistical analysis with respectto cardiac parameters can be used to aid the medical professional inidentifying and/or assessing arrhythmic events (e.g., atrialfibrillation). One further mode of statistical analysis is the“variability mode”. In certain embodiments, the cardiac parametersinvolve heart rate and pressure parameters of the heart. As graphicallyillustrated through a series of plots shown in FIG. 8, a number of heartparameters are individually plotted. The upper plot shows a trend linefor a patient's heart rate over a two and a half minute time period(e.g., from 16:12:14 through 16:14:44). As indicated on the upper plot,the left half of the upper plot shows a high rate event, with heartrates ranging from about 150 beats per minute to about 155 beats perminute. Upon triggering of therapy (e.g., by a corresponding IMD) atabout 16:13:29, the heart rate is shown to steadily decrease.

With reference to the three lower plots, pressure parameters such as RVdiastolic pressure, RV systolic pressure, and ePAD are plotted over thesame time period as described above with respect to the upper plot. Ascan be appreciated, in comparing the heart rate plot to the pressureparameter plots, it is generally shown that the variability of the heartpressure parameters increase significantly during high rate events. Inturn, computation of such pressure variability can be used as aconfirmatory mechanism for the presence of high rate events (e.g.,atrial arrhythmias). In certain embodiments, as illustrated in the RVdiastolic plot, the “variability mode” can determine the variability bycalculating a distance between upper and lower values of a certain timeperiod of one or more of the pressure parameters. As described abovewith respect to FIG. 5, by continuously averaging the deviation of thesecalculated distances from a dynamic base line distance for the patient,the “variability mode” can be used as a confirming factor in identifyingthe presence of arrhythmias. In certain embodiments, this continuousaveraging can be provided with the use of a cumulative sum. In turn, anydeviation between these two distances is averaged over a certainhistorical period of time and such averaged deviation is cumulativelyadded to deviations similarly calculated for prior storage intervals. Assuch, when the calculated distance deviates from the base line distancefor any extended period of time, the cumulative sum is found to show asignificant output. Upon comparing this output to a set threshold level,the “variability mode” would provide an indicator of the presence of anarrhythmia. It should be appreciated that the “variability mode” wouldsimply provide such an indicator, and would generally need to besupported by one or more further criteria in order to be determinative.

FIG. 9 shows a flowchart for one embodiment of the above “variabilitymode” of statistical analysis. As shown, a first step 74 involves agathering of certain data (e.g., “P(i)”) of the one or more cardiacparameters stored within the IMD 10. As mentioned above, this gatheringcan be represented by the IMD 10 gathering such data from the memory ofthe IMD 10 for analysis by the IMD 10, or conversely, by the IMD 10transferring such data from the memory of the IMD 10 to a programmer,monitor, or other processing device for subsequent analysis. Asillustrated above in FIG. 8, in certain embodiments, the data “P(i)”gathered involves a pressure parameter for a specific time period “i”(“i” equaling one initially). A further step 76 involves going throughthe data “P(i)” sequentially to determine the highest value “P(i)h” andlowest value “P(i)l” of the “P(i)” data. In turn, the difference iscalculated between such highest and lowest values. Step 78 involvescomparing the difference calculated in step 76 with a dynamic base linedistance (e.g., a long-term average or a fixed threshold) for thepatient. The deviation between these ratios is subsequently averagedover a certain period of time and such averaged deviation iscumulatively added to deviations similarly calculated for previousstorage intervals. As described above, in certain embodiments, thisaggregate functionality is enabled via the use of a cumulative sum. Asfurther described above, when the calculated ratio deviates from thebase line distance for any extended period of time (e.g., exhibitinghigh rate burden), it is found that the output of the cumulative sumshows a significant output. The output of the cumulative sum “Sp(i)” isfound to be proportional to a magnitude of the high rate burden. Assuch, in step 80, the output of the cumulative sum “Sp(i)” is comparedto a designated threshold “Pt”. If the magnitude exceeds the threshold,the “variability mode” can further provide an indicator as to thepresence of an arrhythmia. As such, during step 82, a counter istriggered, so as to keep track of such indicators during analysis of thedata “P(i)”. In addition, a further counter is triggered to increment“i” by one for subsequent evaluation of the next set of data “P(i+1)”Subsequently, the analysis is looped back to step 74 for furthergathering of data “P(i+1)”. As should be appreciated, if the criteria isnot met in step 80, the further counter is triggered to increment “i” byone in step 84 for subsequent evaluation of the next set of data“P(i+1)”. Subsequently, the analysis is looped back to step 74 foranalysis involving the next data value “P(i+1)”.

It should be appreciated that the exemplified pressure parametersgathered above with respect to the above “variability mode” ofstatistical analysis can be provided from a number of sources and shouldnot be solely limited to IMDs to be within the spirit of the invention.In addition, in certain embodiments, based on the results of thestatistical analyses provided by the above-described modes of analysis,the device providing the modes can provide instruction (e.g.,telemetrically) to IMDs (e.g., implantable drug pumps) instead ofrequiring initial analysis by a medical professional. For example, inthe case of a drug pump, based on the indication and/or duration of anarrhythmic event, the device utilizing the above modes of analysis maybe able to instruct the drug pump to modify the amount of drugs providedto the patient. In addition, in certain embodiments, the specific typeof arrhythmia may also be ascertained via the use of the modes. In turn,in certain embodiments, decisions normally provided by a medicalprofessional with respect to certain diagnoses for the patient (e.g.,regarding levels of medication needed) may instead be automaticallyprovided in using the above modes. Further, it should be appreciatedthat one or more aspects of the modes of statistical analysis describedherein can be combined to provide further modes of statistical analysis.

It will be appreciated the embodiments of the present invention can takemany forms. The true essence and spirit of these embodiments of theinvention are defined in the appended claims, and it is not intended theembodiment of the invention presented herein should limit the scopethereof.

The invention claimed is:
 1. A non-transitory computer-readable mediumprogrammed with instructions for performing a method of identifyingand/or assessing patient arrhythmias, the medium comprising instructionsfor causing a programmable processor to: obtain a set of values, thevalues representative of a heart parameter of a patient collected oversuccessive time intervals, the set gathered from a memory of a medicaldevice implanted within the patient, process the set of values toidentify values that exceed a predetermined amplitude threshold value,such identified values thereby meeting a first criterion; for eachidentified value, compare the identified value with the value from apreceding time interval to determine if such identified value exceedsthe preceding time interval value by a difference threshold amount, suchidentified value thereby meeting a second criterion; and provide anindication of presence of an arrhythmia for storage intervals havingvalues in which the first and second criteria are met.
 2. The computerreadable medium of claim 1, wherein the heart parameter comprises heartrate.
 3. The computer readable medium of claim 1, wherein each value isrepresentative of one of a statistically high, medium, and low measureof the heart parameter collected over a respective time interval.
 4. Thecomputer readable medium of claim 1, wherein each value isrepresentative of a predetermined percentile measure of the heartparameter collected over a respective time interval.
 5. The computerreadable medium of claim 1, wherein the successive time intervalsencompassed by the set of values extend over a predetermined amount oftime.
 6. The computer readable medium of claim 1, wherein the precedingtime interval is the time interval immediately preceding that of theidentified value.
 7. The computer readable medium of claim 1, whereinthe preceding time interval is one of the first, second, and third timeintervals immediately preceding that of the identified value.
 8. Thecomputer readable medium of claim 1, wherein the preceding time intervalincludes the first, second, and third time intervals immediatelypreceding that of the identified value.
 9. The computer readable mediumof claim 1, wherein the predetermined amplitude threshold valuecomprises a value for the heart parameter indicative of an arrhythmia.10. The computer readable medium of claim 1, wherein the differencethreshold amount comprises a difference between the heart parameterduring an arrhythmia and the heart parameter prior to an arrhythmiagenerally corresponding to onset of an arrhythmia.